Method and apparatus for inter-pharmacy workload balancing

ABSTRACT

The system distributes workload amongst a plurality of pharmacy resources that are connected by a computer network. Work orders are queued at each pharmacy resource and redistributed based on existing workload distribution, capacity of pharmacy resources, and/or product demand.

FIELD OF THE INVENTION

The present invention generally relates to workload balancing for pharmacy resources connected by a computer network.

BACKGROUND

Existing pharmacy networks performing order processing may suffer from inefficient distribution of workload. Many factors may contribute to this inefficient distribution. For example, a first location may receive a greater amount of order volume than a second location, equipment at a first facility may be more efficient than in a second facility, or employees at a first location may be more efficient (e.g., better skilled) than in a second retail location. While these pharmacy networks may benefit from redistributing workload, existing pharmacy information systems do not provide this functionality.

Certain retail industries, such as pharmacies, process discrete product orders on the premises of a retail store. Processing of the order may be separated into information processing of the order and physical processing of the order. Because information processing of the order may not need to be performed completely by a single resource and/or at a particular location, the information processing portion of the order fulfillment process may be sent to another resource for execution, e.g., another retail store. This redistribution of work may be especially useful in a franchise retail store network where a corporate entity may have the power to manage distribution and completion of work within the network. However, a distribution system and method may be required to obtain such a workload distribution objective.

SUMMARY OF THE INVENTION

The method and system claimed in the present application provide a process for distributing workload amongst a plurality of pharmacy resources that are connected by a computer network. While the specific method and system will be described to apply to a pharmacy retail network embodiment, it is emphasized that this process may be applied to other retail industries as well.

One embodiment of the claims involves queuing pharmacy prescription orders at each pharmacy resource. Metrics may be taken by a client or a server computer, or may even be taken manually, to determine the workload for each pharmacy resource in the network. A distribution of the current workload may be generated to assist in determining which resources may be overworked and which resources may be under worked. This distribution information may be used to determine a more efficient target workload distribution. The target workload distribution may be implemented by rerouting workload between pharmacy resources in the network. In one embodiment, this may be performed by designating pharmacies as senders or receivers and routing work orders from sender queues to receiver queues.

In another embodiment, workload may be redistributed based on a demand for a drug type and/or a capacity of a pharmacy resource to process orders for that drug type. In this embodiment a pharmacy resource may be assigned or designated a specific order process function to perform and work orders may be routed to the pharmacy resource for processing a portion of work related to the function. For example, a pharmacy resource may be designated a receiver for prescription orders having a certain drug type. Consequently, work orders for the drug type may be rerouted to the pharmacy resource for at least a portion of the order processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a diagram of a data network that may operate in accordance with a described embodiment;

FIG. 2 illustrates an embodiment of the network computer of FIG. 1;

FIG. 3 illustrates an embodiment of a pharmacy computer system;

FIG. 4 illustrates a pharmacy network embodiment;

FIGS. 5 aand 5 b illustrate possible pharmacy information processing flows;

FIG. 6 illustrates an embodiment of a pharmacy network designation system;

FIG. 7 illustrates a workload distribution graph that may be used to analyze an existing workload distribution;

FIG. 8 illustrates a flow chart of the workload distribution process;

FIG. 9 illustrates an embodiment that uses a central repository queue;

FIG. 10 illustrates a resource assignment process;

FIG. 11 illustrates a resource assignment system;

FIG. 12 illustrates a resource assignment table; and

FIG. 13 illustrates a distributed processing environment for a pharmacy network.

DETAILED DESCRIPTION

Although the following text sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the invention is defined by the word of the claims set forth at the end of this patent. The detailed description is to be construed as exemplary only and does not describe every possible embodiment since describing every possible embodiment would be impractical, if not impossible. Numerous alternative embodiments could be implemented, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope the claims.

It should also be understood that, unless a term is expressly defined in this patent using the sentence “As used herein, the term ‘______’ is hereby defined to mean . . .” or a similar sentence, there is no intent to limit the meaning of that term, either expressly or by implication, beyond its plain or ordinary meaning, and such term should not be interpreted to be limited in scope based on any statement made in any section of this patent (other than the language of the claims). To the extent that any term recited in the claims at the end of this patent is referred to in this patent in a manner consistent with a single meaning, that is done for sake of clarity only so as to not confuse the reader, and it is not intended that such claim term be limited, by implication or otherwise, to that single meaning. Finally, unless a claim element is defined by reciting the word “means” and a function without the recital of any structure, it is not intended that the scope of any claim element be interpreted based on the application of 35 U.S.C. § 112, sixth paragraph.

FIG. 1 illustrates an embodiment of a data network 10 including a first group of pharmacies 20 operatively coupled to a network computer 30 via a network 32. The plurality of pharmacies 20 may be located, by way of example rather than limitation, in separate geographic locations from each other, in different areas of the same city, or in different states. The work 32 may be provided using a wide variety of techniques well known to those skilled in the art for the transfer of electronic data. For example, the network 32 may comprise dedicated access lines, plain ordinary telephone lines, satellite links, combinations of these, etc. Additionally, the network 32 may include a plurality of network computers or server computers (not shown), each of which may be operatively interconnected in a known manner. Where the network 32 comprises the Internet, data communication may take place over the network 32 via an Internet communication protocol.

The network computer 30 may be a server computer of the type commonly employed in networking solutions. The network computer 30 may be used to accumulate, analyze, and download pharmacy data. For example, the network computer 30 may periodically receive data from each of the pharmacies 20 indicative of information pertaining. to a prescription order, billing information, employee data, etc. The pharmacies 20 may include one or more facility servers 36 that may be utilized to store information for a plurality of customers/employees/accounts/etc. associated with each facility.

Although the data network 10 is shown to include one network computer 30 and three pharmacies 20, it should be understood that different numbers of computers and pharmacies may be utilized. For example, the network 32 may include a plurality of network computers 30 and dozens of pharmacies 20, all of which may be interconnected via the network 32. According to the disclosed example, this configuration may provide several advantages, such as, for example, enabling near real time uploads and downloads of information as well as periodic uploads and downloads of information. This provides for a primary backup of all the information generated in the process of updating and accumulating pharmacy data.

Fig. 2 is a schematic diagram of one possible embodiment of the network computer shown in FIG. 1. The network computer 30 may have a controller 50 that is operatively connected to a database 52 via a link 56. It should be noted that, while not shown, additional databases may be linked to the controller 50 in a known manner.

The controller 50 may include a program memory 60, a microcontroller or a microprocessor (MP) 62, a random-access memory (RAM) 64, and an input/output (I/O) circuit 66, all of which may be interconnected via an address/data bus 70. It should be appreciated that although only one microprocessor 62 is shown, the controller 50 may include multiple microprocessors 62. Similarly, the memory of the controller 50 may include multiple RAMs 64 and multiple program memories 60. Although the I/O circuit 66 is shown as a single block, it should be appreciated that the I/O circuit 66 may include a number of different types of I/O circuits. The RAM(s) 64 and programs memories 60 may be implemented as semiconductor memories, magnetically readable memories, and/or optically readable memories, for example. The controller 50 may also be operatively connected to the network 32 via a link 72.

FIG. 3 is a schematic diagram of one possible embodiment of several components located in one or more of the pharmacies 20 from FIG. 1. Although the following description addresses the design of the pharmacies 20, it should be understood that the design of one or more of the pharmacies 20 may be different than the design of other pharmacies 20. Also, each pharmacy 20 may have various different structures and methods of operation. It should also be understood that the embodiment shown in FIG. 3 illustrates some of the components and data connections present in a pharmacy, however it does not illustrate all of the data connections present in a typical pharmacy. For exemplary purposes, one design of a pharmacy is described below, but it should be understood that numerous other designs may be utilized.

The pharmacies 20 may have a facility server 36, which includes a controller 80, wherein the facility server 36 is operatively connected to a plurality of client device terminals 82 via a network 84. The network 84 may be a wide area network (WAN), a local area network (LAN), or any other type of network readily known to those persons skilled in the art. The client device terminals 82 may also be operatively connected to the network computer 30 from FIG. 1 via the network 32.

Similar to the controller 50 from FIG. 2, the controller 80 may include a program memory 86, a microcontroller or a microprocessor (MP) 88, a random-access memory (RAM) 90, and an input/output (I/O) circuit 92, all of which may be interconnected via an address/data bus 94. As discussed with reference to the controller 50, it should be appreciated that although only one microprocessor 88 is shown, the controller 80 may include multiple microprocessors 88. Similarly, the memory of the controller 80 may include multiple RAMs 90 and multiple programs memories 86. Although the I/O circuit 92 is shown as a single block, the I/O circuit 92 may include a number of different types of I/O circuits. The RAM(s) 90 and programs memories 86 may also be implemented as semiconductor memories, magnetically readable memories, and/or optically readable memories, for example.

The client device terminals 82 may include a display 96, a controller 97, a keyboard 98 as well as a variety of other input/output devices (not shown) such as a scanner, printer, mouse, touch screen, track pad, track ball, isopoint, voice recognition system, etc. Each client device terminal 82 may be signed onto and occupied by a pharmacy employee to assist them in performing their duties. Pharmacy employees may sign onto a client device terminal 82 using any generically available technique, such as entering a user name and password. If a pharmacy employee is required to sign onto a client device terminal 82, this information may be passed via the link 84 to the facility server 36, so that the controller 80 will be able to identify which pharmacy employees are signed onto the system and which client device terminals 82 the employees are signed onto. This may be useful in monitoring the pharmacy employees'productivity.

Typically, facility servers 36 store a plurality of files, programs, and other data for use by the client device terminals 82 and the network computer 30. One facility server 36 may handle requests for data from a large number of client device terminals 82. Accordingly, each facility server 36 may typically comprise a high end computer with a large storage capacity, one or more fast microprocessors, and one or more high speed network connections. Conversely, relative to a typical facility server 36, each client device terminal 82 may typically include less storage capacity, a single microprocessor, and a single network connection.

FIG. 4 illustrates a pharmacy network 400 comprising a network computer 403, or network node, at each pharmacy location 410, 420, 430. This network computer may be connected to a scanner 404 and may have a connection link 412 to other pharmacy computers via a network 450. An employee 402 may receive a physical prescription order 401 from a customer at a particular pharmacy location 410 and input the prescription order 401 into the network computer 403 for that location. The employee 402 may scan the prescription as well as associated documents into the computer in addition to manually entering prescription information. The network computer 403 may have a set of processed prescriptions stored in a queue 405. The employee 402 may continue entering prescriptions into the queue 405 as they are received.

The prescriptions that have finished information processing are ready for physical processing to fill the prescription. The physical process of filing the prescription at the retail location may begin with the printing of a prescription label 411 from the queue 405. The scheduling and printing may be automated, and may be in accordance with the process described in U.S. application Ser. No. ______, entitled, “System For Separating And Distributing Pharmacy Order Processing,” taking into account desired delivery times, customer waiting requirements, etc. Based on the label 411, and/or instruction set, a pharmacist 406 may physically prepare the drug by mixing compounds 407 to produce a final prescription drug, receive pre-processed compounds and formulations, or otherwise obtain the materials necessary to fill the prescription 408 based on the label. The queue 405 may be operated as a first in, first out (FIFO) stack process, where newly entered orders are placed on top of the queue while orders are pulled from the bottom of the queue for filling.

In a pharmacy embodiment, work for each prescription order may be divided between physical preparation of a prescribed drug and the processing of prescription information required to prepare the drug. For example, referring to FIG. 4, physical preparation involves performing various tasks generally illustrated by 406, 407, and 408. Information processing is captured by 401-405. The information processing may be further divided as illustrated in FIG. 5 a. FIG. 5 a illustrates a possible segmentation and processing of information. The information processing of a prescription may include, but is not limited to, inputting prescription data 500, authenticating a prescription 501, validating customer information 502, validating third-party provider information 503, collecting payment information 504, referencing drug information 505, determining out-of-stock status of materials 506, and entering accounting information into an accounting database 507.

The information processing portion of work may be performed by multiple pharmacy resources, e.g., pharmacy employees, at different locations. Therefore, an embodiment may distribute this work portion amongst a number of pharmacy resources to improve overall network processing efficiency. While FIG. 5 a illustrates that these processing blocks may be performed serially, these steps may be performed in parallel as illustrated in FIG. 5 b. FIG. 5 b illustrates that after any one of the work portions are performed, the work for that order may be routed 510 to another resource or location for further processing, e.g., for finishing another portion of the work.

Referring again to FIG. 4, a portion or all of the information processing associated with a prescription may be routed to a different pharmacy resource, such as store locations 420 or 430, than the location 410 in which the prescription is first entered. Once the information processing is completed, the instructions may be sent to another resource or location for label printing and physical preparation, including the originating store. Alternatively, the information processing resource may complete the prescription by physically preparing and delivering the drug at that location. Alternatively, delivery may occur, for example, from a mail service facility or home delivery facility of the pharmacy network. Delivery may be completed at a different store, when the customer chooses to pick up the order from another store.

The process of distributing information processing for a network of pharmacies will now be described. The workload for each pharmacy may be determined in a number of ways. Generally, workload may be determined by determining the amount of work performed in a given amount of time. Because one of the described embodiments is concerned with pharmacy related work and pharmacy efficiency, a workload calculation that uses a pharmacy related work factor and pharmacy efficiency factor is useful. Pharmacy workload may be determined as the ratio of the number of prescriptions filled to the total number of man hours for a given store: ${Pharmacy\_ Workload} = \frac{{Number\_ of}{\_ prescriptions}}{{Total\_ man}{\_ hours}}$

Man hours may be calculated as a unit of one hour's work performed by an average pharmacy employee, which may be adjusted or weighted based on the type of employee, e.g., a pharmacist, a pharmacy technician, clerks, etc.

The prescription volume may be manually tabulated, or determined by a computer. For example, the network computer may simply sum the number of prescriptions filled for each day and use this number. The computer may calculate the average number of prescriptions being fulfilled over a shorter or longer time period as desired. If a manual collection system is implemented, a reasonable amount of time for collecting workload data may be a 1-2 week period. For automated systems, the collection time may be made arbitrarily small, e.g., on the order of hours, minutes, seconds, etc.

As illustrated in FIG. 6, the workload for each pharmacy may be calculated and collected by at least one network computer, e.g., 600, 610, 620, 630, 640. The network computer that collects the workload data may be a network computer at a particular retail store (e.g., 610, 620, 630, 640) and/or a network computer, e.g., 600, at an analysis center that does not perform prescription processing. Among other factors, the prescription count used in the workload calculation may consist of a total number of filled prescriptions in a given amount of time, an average number of prescriptions being processed at a given time, or an average number of prescriptions waiting in a queue for a given amount of time.

Once the workload information from the pharmacies is collected, a current workload distribution 601 of a network of pharmacies may then be determined and a redistribution strategy may be analyzed. The analysis may be facilitated by using a distribution graph as illustrated in FIG. 7. Based on the distribution, workload may be redistributed from high workload stores to low workload stores. This may be done to achieve a target workload distribution. For example, in situations where the median distribution is below a certain prescription/man-hour rate, work may be redistributed in order to move the median higher in order to achieve a target average workload. In order to accomplish this, certain pharmacy resources may be identified and designated as receivers or senders. A pharmacy resource may include, for example, employees, equipment, technology, etc. While one embodiment may consider a single store location as a pharmacy resource, in which case the set of resources associated with the store may be considered the pharmacy resource, a pharmacy resource may include pharmacists not necessarily associated with a particular store location, for example, remote pharmacists that work from home. Factors that may be considered in determining a target workload distribution include overhead costs of a pharmacy-location, labor costs, taxes, and efficiency factors.

In one embodiment, stores in which workload needs to be redistributed from may be designated as sender stores/resources, while stores in which workload needs to be redistributed to may be designated as receiver stores/resources. Sender stores may be stores in which the workload is higher than a given threshold or average, while receiver stores may be stores in which the workload is lower than a given threshold or average. Thresholds may be determined based on the current workload distribution and target workload distribution. As illustrated in FIG. 6, the designation of a store may be a parameter stored in a central location, such as a table 602 in a server machine 600, and/or a parameter set in a network computer such as 610, 620, 630, or 640. The designations may be manually set or overridden based on an administrator input 603.

Once the stores/resources are designated as senders or receivers, a sender network computer may begin taking unprocessed prescription orders from its queue and sending them to a receiver store/resource queue. The number of prescriptions sent or accepted may be based on a workload level and routing rate that is adjusted in order to meet a target distribution for a particular period of time. For example, a first store may have a workload of 8 prescriptions/man-hour, while the target workload is set at 6 prescriptions/man-hour. The system may set a routing rate such that the first store sends prescriptions to a set of receivers until its workload stabilizes at 6 over a period of time.

Metrics may be taken on a periodic basis such that when a threshold is met, the designation of a store automatically changes. The shorter the period for recalculating workload distribution, the quicker the response for a change in store volume or manpower. For example, should the above mentioned store begin to average only 5 prescriptions/man-hour, the store may be changed from a sender to a receiver. An alternate designation may be non-participating, or neutral. This designation may be placed when the store is performing within a target range at a particular efficiency or when the store is performing within a target workload range. In the above example, if the store is operating at 6 prescriptions/man-hour without intervention (or within a lower and upper threshold around 6 prescriptions/man-hour), it may be designated as non-participating or neutral. It should be noted that the neutral designation may be placed on a store for other reasons, e.g., neutral may be placed on stores that are non-functioning, or non-participating.

The flowchart of FIG. 8 illustrates a designation process. The process starts at block 801, where pharmacy workload metrics are acquired. As mentioned above, this acquisition of metrics may consist of generating a workload distribution. From these metrics, a target workload distribution may be determined. In the embodiment illustrated in FIG. 8, a target workload distribution may result in the determination of a set of threshold values (e.g., maximum and minimum workload values for each store). In blocks 802, 803, and 807, the workload from each store is read. If the end of the list 803 is reached, the process ends 807. Block 804 checks to see if the current store has a workload between a set upper and lower threshold bound. If the workload is within bounds, block 806 designates a store as neutral if it is not already designated neutral in block 805. Block 808 determines if the store workload is below a lower threshold. If so, block 810 designates the store as a receiver, if it is not already designated as a receiver in block 809. If the store workload is greater than an upper threshold, block 812 designates the store as receiver, if it is not already designated a receiver in block 811.

When a store switches designations, it may continue processing the work placed in its queue even if the work is from another pharmacy. However, if the store has been switched from a sender to a receiver or from a receiver to a sender, priority routing may be performed based on ownership of the order. For example, in a store that has switched from a receiver to a sender, the store may first send off work that was not originated from its store. Also, if a prior sender has become a receiver and a prior receiver has become a sender the current sender may direct work back to the receiver that sent it.

FIG. 9 illustrates an embodiment in which a central server 900 may implement a queue 901 that accepts routed work orders from sender queues. The server acts as a central redistribution repository that will accept work requests from each receiver. In this way, when workload changes induce store designation changes, redistribution is just a matter of changing between work requests from a central location and transmitting work orders to the central location. In this embodiment, completion of information processing for a particular order may trigger printing of a label or instruction set at the printer queue of a distribution resource, e.g., a pharmacy location, a mail order facility, etc., for filling and dispensing.

Additional metrics may be used to determine the efficacy of a workload distribution. For example, a cost metric may be used to determine if the increase in the average prescriptions/man-hour rate for the network translates into a cost savings. This may be done by factoring in the number of prescriptions sent to the receiving stores, the number of prescriptions completed by the receiving stores, and then calculating the financial benefit to a store based on possible missed prescriptions. This report may be called a missed opportunity report.

Workload distribution may be based on a capacity of a network resource to process product demand. In the case of a pharmacy network system, demand may be based on the number of prescriptions that need to be processed in a given amount of time, e.g., a promised delivery time. (Queued prescriptions may be arranged by promised time, and consequently, demand may be based on the promised times of prescription orders in a queue.) Pharmacy resource capacity may be based on the rate at which the resource can process a number of prescriptions. This rate may be estimated based on historic data for the resource. For example, metrics may be taken to determine the average time necessary for a pharmacy resource to process a number of prescription orders. The capacity of the resource can then be estimated by calculating the amount of time necessary to process the prescription orders in its queue and comparing this to a desired time, such as a promised delivery time, for the prescription orders. If the estimated finish time approaches or exceeds the desired time, then the pharmacy resource may have insufficient capacity and be designated as a sender. If the estimated finish time is less than the desired time by a threshold, then the pharmacy resource may have excess capacity and may be designated as a receiver. The threshold may be based, for example, on the amount of time needed for a resource to process an additional order within the desired time for that order. In one embodiment, the promised time of queued work orders for a pharmacy resource may be averaged and the pharmacy resource may be designated a receiver or sender based on whether the averaged promised time is above or below a threshold.

Workload distribution may be further based on product type and resource type. In a pharmacy system comprising a network of pharmacy resources, each pharmacy may be outfitted with identical equipment, inventory, and personnel for processing standard drug prescription orders. However, there are non-standard or non-traditional drugs that may require different equipment, special materials, and/or different technical expertise to process. Outfitting every facility with similar equipment and inventory to account for non-traditional drugs could be prohibitively expensive. Providing expert personnel at each store location in a network may also be difficult, if not impossible. Also, more often than not, the demand for non-traditional drugs and even a portion of traditional drugs, is not substantial enough to justify an additional expenditure in equipment, inventory, and human resources for each store.

A pharmacy system may thus assign or designate certain resources, e.g., facilities or personnel, to process a portion of a prescription order based on drug type. The assignment may be based on a demand for the drug type and/or a capacity of a resource to process the drug type. In this case, the capacity of the resource may be based on the efficiency and cost of using the pharmacy resource, not just its capacity to fill a demand in a given time. A workload distribution system may accordingly route prescription orders for specific drug types primarily to the designated pharmacy resources having the capacity to process the drug types, thereby leveraging pharmacy resource expertise and/or economies of scale. The designated pharmacy resource may be called a process center for the specific drug type.

In one embodiment, assigning a pharmacy resource to process the prescription order may be based on minimizing a difference between the system demand for the drug type and the capacity of a set of pharmacy resources. FIG. 10 illustrates this embodiment. Metrics are acquired in a first block 1001. The demand for a drug type is determined 1002 by taking the total number of prescription orders in the network associated with a drug type. The capacity of pharmacy resources assigned to process the drug type may be considered by first calculating an estimated time for processing the prescription orders for the drug type 1003. When the estimated time of processing by the assigned resources is less than a desired time by a threshold amount (e.g., a safety margin) no further assignments are necessary 1005. When a single pharmacy resource is insufficient to process the demand in a desired time 1006, more pharmacy resources may be assigned to process the drug type 1007. When demand for a drug type falls, the system may determine that less pharmacy resources need to be allocated to process a drug type and may reduce the number of designated stores 1008 processing that drug type. This may be done incrementally and may be based on a set of thresholds that are determined based on safety margins. Alternatively, in addition to calculating the estimated time for processing the demand, a capacity measure may include calculating a cost efficiency. For example, given that a first pharmacy resource and a second pharmacy resource may process the demand in the desired time, the system may determine that the first pharmacy resource is cheaper to use than the second resource and assign the first resource as a process center. The system may then route prescriptions primarily to the first resource.

FIG. 11 illustrates a computer system for assigning functions to a network of pharmacy resources. A server 1100 may receive demand data 1101 and capacity data 1102 from a set of pharmacy client computers 1110-1140 associated with pharmacy resources. The demand data and capacity data may be used to determine assignments/designations 1103 for pharmacy resources. These assignments may be stored in a table 1104 or other data object. A business expert 11 05 may provide parameters used to help determine the assignments. Table 1104 illustrates that each pharmacy resource may function in more than one capacity and may have multiple designations. For example, pharmacy location 2, 3, and 4, as illustrated in table 1104, may function both as retail stores receiving or sending traditional prescription orders as well as a processing center for a particular function. (It should be noted that a pharmacy resource may have more than two designations.)

When a pharmacy resource is designated as a process center, that pharmacy'resource may be a primary receiver for prescription orders associated with a drug type that the pharmacy resource is assigned to process. If there is only one pharmacy resource assigned to process a drug type, for example compound drugs, then all compound drug prescriptions in the network may be routed to that pharmacy resource. However, if demand exceeds the capacity of the single resource, more pharmacy resources may be assigned to handle the workload. This is the case illustrated in 1104, where two pharmacy resources (locations 3 and 4) are designated pet centers.

Determining capacity of the resource may involve determining the existence of equipment, availability of materials at the facility to process the drug type, and availability of the equipment. Availability of the equipment may be based on the existing workload of the equipment. Availability of equipment may also be based on a configuration of the equipment. For example, identical equipment may be used to process two different drug types, but a different equipment setup may be required to produce each drug type. In this case, equipment capacity may take into account a transition time required to configure the equipment to process a prescription order associated with a different drug type. Efficiency of the pharmacy resource or equipment may also be based on the transition time.

A pharmacy resource may be an individual pharmacist. Pharmacists for performing specialty drug processing may be located in a number of different locations, including retail stores, other specialty stores, or home locations. A distribution table such as 1200 in FIG. 12, may be accessed to determine pharmacist assignments 1250, availability information 1230, and location 1220. Whenever additional pharmacists are needed to handle increased demand or other workload events, the distribution table may be accessed to determine which additional resources may be activated/assigned. The table may be stored on a server and updated accordingly.

In one embodiment, determining the capacity of a pharmacist may include determining the availability of the pharmacist to process a drug type. This may be based on a pharmacist work schedule 1230. Determining the capacity of the pharmacist may also be based on an expertise level 1240 of the pharmacist and a labor cost of the pharmacist. The expertise level may be based on a certification of a pharmacist to perform processing for specialized drugs. This certification may be based on legal standards. The expertise level may also be determined by a pharmacy company. For example, the expertise level may be based on an accuracy rating of the pharmacist and/or efficiency of the pharmacist, where efficiency may be based on the pharmacist's rate of processing a specific drug type.

FIG. 13 illustrates a pharmacy network where process functions are assigned to specialized pharmacy resource units. In this network, a retail store A 1301 may be connected to a set of neighbor retail stores 1302. The network may also have specialized facilities such as a special processing center (“SPC”) 1303 which may be a separate facility that houses a set of experts. This set of experts may be more efficient at processing specialty drugs. The work orders for specialty drugs may be routed primarily to the SPC, thereby aggregating specialty drug processing at a cost efficient resource. Additionally, there may be facilities that contain specialized equipment for less common medications such as pet medicine or compound drugs. These resources may also be housed in separate facilities, e.g., a pet center 1304 or a compound drug center 1305 and prescriptions orders for pet medication and compound drugs may be routed to their respective processing centers for faster processing. Also, consolidation of certain common process functions, such as fulfillment and mailing, may also increase the speed at which general prescriptions are processed. Thus, a mail fulfillment facility (“MFC”) 1306 having both equipment and personnel focused solely on fulfillment and mailing may also increase network efficiency.

While FIG. 13 illustrates a single function assignment to a pharmacy resource, e.g., pet, specialty, and compound centers, these facilities are not necessarily limited to only one function. For example, an SPC facility may have the capability to also serve as a retail store. This cross-functional capacity allows for assignment of functionality based on network demand and efficiency. For example, while the equipment to process compound drugs may not be provided at each facility, a subset of retail facilities may hold equipment capable of processing compound drugs. Because the demand for compound drugs may not justify the cost of operating all the equipment at one time, only a subset of stores may be assigned to process compound drugs. Thus, in one embodiment, when demand for compound drugs is not high, only a few of the resources having compound making capabilities may be designated as compound centers and process compound prescription orders Meanwhile, the non-assigned facilities may operate in another capacity. When demand increases, more stores/facilities having compound equipment may be made available and designated as compound facilities to process the extra demand, even while the same facility continues to function in other roles.

Workload distribution may also be based on anticipated capacity changes of a pharmacy resource. For example, in emergency situations, (e.g., a natural disaster causing evacuation of pharmacists) work may be manually redirected to non-affected locations and stores. In another example, there may be anticipated intermittent staffing and/or availability changes (e.g., equipment maintenance periods, staff vacations, etc.) which may cause the efficiency of a resource to decrease. In this situation, a target workload distribution may involve placing a smaller workload on pharmacy resources in which there is an anticipated shortage in capacity. The determination of the target workload distribution may also consider the workload backlog at a particular location and adjust that location up or down accordingly. It should be noted that an anticipated staffing shortage may be accounted for when calculating the workload for the store and/or when determining an appropriate target workload distribution. Furthermore, in addition to anticipating staffing changes, an embodiment of the claims may proactively adjust staffing in order to effect workload of a pharmacy to achieve a target workload distribution of the network. 

1. A method of managing drug prescription orders within a network of pharmacy resources connected by an information system, the method comprising: receiving work orders at each pharmacy resource into a queue for execution; calculating a workload for each pharmacy resource; determining a current workload distribution of the plurality of pharmacy resources based on the workload calculation of each pharmacy resource; designating a pharmacy resource as one of a sender and a receiver based on the workload of the pharmacy resource and the workload distribution; and routing work orders from a sender queue to at least one receiver queue based on the work load distribution.
 2. The method of claim 1, wherein the current workload is based on a number of current work orders and a capacity of a pharmacy resource to complete the work orders.
 3. The method of claim 1, wherein the workload of each pharmacy resource is based on a number of prescriptions handled by the pharmacy resource per available man-hour of the pharmacy resource.
 4. The method of claim 1, wherein routing work orders from a sender queue to at least one receiver queue is based on whether a prescriptions per man hour of the receiver is greater than that of the sender.
 5. The method of claim 1, wherein designating a pharmacy resource comprises designating a pharmacy resource as a sender when the pharmacy resource workload is greater than an average workload of the plurality of pharmacy resources by a threshold amount, and designating a pharmacy resource as a receiver when the pharmacy resource workload is less than an average workload of the plurality of pharmacy resource by a threshold amount.
 6. The method of claim 1, wherein designating a pharmacy resource comprises setting a participation flag in a database table.
 7. The method of claim 1, wherein calculating a workload for each pharmacy resource comprises calculating a workload based on one or more of overhead costs of a pharmacy location, labor costs, taxes and an efficiency factor.
 8. The method of claim 1, further comprising anticipating a change in capacity of a pharmacy resource to process workload and designating the pharmacy resource as a sender when anticipating a decrease and designating the pharmacy resource as a received when anticipating an increase.
 9. The method of claim 1, wherein the work orders comprise a promised time and further wherein received work orders are arranged in the queue based on promised time.
 10. The method of claim 9, further comprising designating a pharmacy resource a receiver if the pharmacy resource has queued work orders with an average promised time greater than a threshold, and designating the pharmacy receiver a sender if the promised time is less than a threshold.
 11. The method of claim 1, further comprising adjusting pharmacy staffing schedules based on at least one of the workload of a pharmacy resource and the workload distribution.
 12. A system for distributing pharmacy prescription processing workload amongst a plurality of pharmacy locations, the system comprising: a client computer located at each pharmacy location, wherein the client computer is programmed to accept work orders into a queue and calculate a workload for the pharmacy location; and a server computer for collecting workload data from the plurality of pharmacy locations, wherein the server computer determines a target workload distribution based on a current workload distribution and designates each client computer at each pharmacy location as one of a sender and a receiver; wherein each client computer designated as a sender routes work orders to at least one client computer designated as a receiver based on the target workload distribution.
 13. The system of claim 12, wherein the workload is based on a number of prescriptions handled by the pharmacy location per available man-hour for the pharmacy location.
 14. The system of claim 12, wherein the target workload distribution comprises an average threshold workload.
 15. The system of claim 12, wherein the server computer stores the designation of each client computer in a designation table and further wherein each client computer accesses the designation table to determine its designation.
 16. The system of claim 12, wherein the server computer provides a user the ability to manually override a designation in the designation table.
 17. The system of claim 12, wherein the target-workload is based on an anticipated resource capacity change.
 18. A computer-readable medium having computer-executable instructions for distributing pharmacy workload across a plurality of pharmacy resources, the executable instructions comprising: a first routine receiving work orders for a first pharmacy location into a queue for execution; a second routine calculating a workload for the first pharmacy location; a third routine receiving workload distribution data; and a fourth routine routing work orders between the first pharmacy location queue and a second pharmacy location queue based on the received workload distribution data.
 19. The computer-readable medium of claim 18, wherein the workload comprises a ratio of a number of prescriptions handled by the first pharmacy location per available man-hours for the location.
 20. The computer-readable medium of claim 18, wherein the workload distribution criteria comprises at least one of overhead costs, pharmacist salaries, taxes, and efficiency of personnel. 